In this chapter, the book is concluded with a synthesis of key findings, reflections on the research questions, and a discussion of limitations and future directions. The main contributions of this work are summarized in Sect. 6.1, where the results of the experiments are revisited in light of the overall objectives of the study. Particular emphasis is placed on the implications of the data-driven and transfer learning approaches for improving harmful content detection in data-scarce and subjective NLP tasks. Section 6.2 provides answers to the five research questions initially posed, highlighting how each experimental outcome contributes to a deeper understanding of the role of preprocessing, model architecture, and affective features in detecting fake news and hate speech. The chapter also reflects on the practical applicability of the proposed methods, especially in real-world social media contexts. In Sect. 6.3, the limitations of the current study are acknowledged, including challenges related to subjectivity in annotation, domain adaptation, and generalizability across languages and platforms. Several avenues for future research are proposed, such as leveraging multimodal data, improving sentiment representation, and developing more interpretable models for harmful content detection. These directions aim to further personalize and refine automated moderation systems, ultimately contributing to more responsible and adaptive NLP applications.